Metaheuristics Based Optimization Technique for Protein-Ligand Docking

Virtual screening using molecular docking requires optimization, which can be solved by using metaheuristics methods. Typically the interaction between two compounds is calculated using computationally intensive Scoring Functions (SF) which is computed in several spots which are called as binding surfaces. In this paper we present a novel approach for molecular docking which is based on parameterized and parallel metaheuristics which is useful in leveraging heterogeneous computing based on heterogeneous architectures. The approach decides on the optimization technique at running time by setting up a new configuration schema that allows parallel offloading of the data intensive sections of the docking. Hence the docking process is carried out in parallel efficiently while performing the metaheuristics execution. The approach carries out docking and computations of molecular interactions required for SF in parallel so that the time efficiency is improved. This opens a new path for further developments in virtual screening methods in heterogeneous platform.

Implementing cloud-based parallel metaheuristics: an overview

Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel im- plementation applying HPC techniques is a common approach for efficiently using available resources to re- duce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuris- tics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.